Probabilistic methods and apparatus to determine the state of a media device

ABSTRACT

Probabilistic methods and apparatus to determine the state of a media device are described. An example method to determine the state of a media device includes processing a first output signal from a first sensor measuring a first property of the media device to generate a first parameter value and processing a second output signal from a second sensor measuring a second property of the media device to generate a second parameter value. Next, the example method includes combining first conditional probabilities of the first parameter value and the second parameter value to determine a first state probability for the first state of the media device and combining second conditional probabilities of the first parameter value and the second parameter value to determine a second state probability for the second state of the media device. Then, determining the state of the media device by selecting the greater of the first state probability or the second state probability.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to audience measurement and,more particularly, to probabilistic methods and apparatus to determinethe state of a media device.

BACKGROUND

Media ratings and metering information enables media producers and mediadevice manufacturers to improve their media programming or media devicefunctionality and also enables media producers to determine a price tobe charged for advertising broadcast during such media programming. Inaddition, accurate media device usage demographics enables advertisersto target audiences of a desired size and/or audiences composed ofmembers having a set of common, desired characteristics (e.g., incomelevel, lifestyles, interests, etc.).

Metering information and media ratings are typically generated bycollecting media consumption or exposure information from a group ofstatistically selected individuals and/or households. Each of thestatistically selected individuals and/or households typically has adata logging and processing unit commonly referred to as a “meter.” Inhouseholds of individuals having multiple media devices, the datalogging and processing functionality may be distributed among multiplemeters or metering units, where each metering unit may be provided foreach media device or media presentation area. The metering unit includessensors to gather data from the monitored media devices (e.g.,audio-video (AV) devices) at the selected site.

The collected media device data is subsequently used to generate avariety of information or statistics related to media device usageincluding, for example, audience sizes, audience demographics, audiencepreferences, the total number of hours of media device usage perindividual and/or per region, etc.

The configurations of automated metering systems vary depending on theequipment used to receive, process, and display media device signals ineach environment being monitored. For example, homes that receive cabletelevision signals and/or satellite television signals typically includea metering unit in the form of a set top box (STB) to receive mediasignals from a cable and/or satellite television provider. Media systemsconfigured in this manner are typically monitored using hardware,firmware, and/or software to interface with the STB to extract or togenerate signal information. Such hardware, firmware, and/or softwaremay be adapted to perform a variety of monitoring tasks including, forexample, detecting the channel tuning status of a tuning device disposedin or otherwise associated with the STB, extracting programidentification codes embedded in media signals received at the STB,generating signatures characteristic of media signals received at theSTB, etc. However, many audio/video media systems that include an STBare configured such that the STB may be powered independent of the mediadevice (e.g., an audio/video presentation device). As a result, the STBmay be turned on (i.e., powered up) and continue to supply media signalsto the media device even when the media device is turned off.Additionally, while the media device may be on, the media device may beused for gaming or watching DVDs. Thus, the monitoring of media deviceshaving independent power supplies or more than one active statetypically involves an additional device or method to determine theoperational status of the media device to ensure that the collected datareflects information about the signals from the metered media device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a metering system including an exampleNaïve Bayes Module monitoring a media device.

FIG. 2 is a block diagram showing the example Naïve Bayes Module of FIG.1.

FIG. 3 is a block diagram showing an example implementation of the NaïveBayes Module of FIG. 2 including sensors and parameters.

FIG. 4 is a table showing an example training set for the example NaïveBayes Module of FIG. 2.

FIG. 5 is a table showing trained weights for the example Naïve BayesModule of FIG. 2 derived from the training set of FIG. 4.

FIG. 6 is a table showing example parameter values from sensorsmonitoring a media device and a calculated state of the media device.

FIG. 7 is a flowchart representative of an example process that may beperformed by, for example, a processor to implement a training routinefor the example Nave Bayes Module of FIG. 2.

FIGS. 8 and 9 are flowcharts representative of example process that maybe performed by, for example, a processor to implement any portion orall of the example Nave Bayes Module of FIG. 2.

FIG. 10 is a schematic illustration of an example processor platformthat may be used and/or programmed to implement the example Naïve BayesModule of FIG. 2 and/or to use a Naïve Bayes algorithm to determine thestate of media devices and/or the example process of FIGS. 7, 8, and/or9 to implement any or all of the example system and methods describedherein.

DETAILED DESCRIPTION

Example probabilistic methods and apparatus to determine the state of amedia device are described. In one example, a method involves processinga first output signal from a first sensor measuring a first property ofthe media device to generate a first parameter value and processing asecond output signal from a second sensor measuring a second property ofthe media device to generate a second parameter value. Next, the methodcombines first conditional probabilities of the first parameter valueand the second parameter value to determine a first state probabilityfor the first state of the media device, wherein the first conditionalprobabilities are representative of probabilities the first parametervalue corresponds to the media device being in a first state. The methodfurther combines second conditional probabilities of the first parametervalue and the second parameter value to determine a second stateprobability for the second state of the media device, wherein the secondparameter conditional probabilities are representative of probabilitiessecond parameter value corresponds to the media device being in a secondstate. Then, the method determines the state of the media device byselecting the greater of the first state probability or the second stateprobability.

Prior to receiving the first output signal from the first sensor, thefirst conditional probabilities are calculated. The first conditionalprobabilities of the first parameter value and the second parametervalue are determined by placing a training media device into the firststate, recording a first set of first parameter values, recording asecond set of second parameter values, calculating a probabilitydistribution of the first parameter values in the first set, andcalculating a probability distribution of the second parameter values inthe second set. Likewise, the second conditional probabilities of thefirst parameter value and the second parameter value are determined byplacing the training media device into the second state, recording athird set of first parameter values, recording a fourth set of secondparameter values, calculating a probability distribution of the firstparameter values in the third set, and calculating a probabilitydistribution of the second parameter values in the fourth set.

In another example, an apparatus includes a parameter generator toprocess a first output signal from a first sensor measuring a firstproperty of the media device to generate a first parameter value and toprocess a second output signal from a second sensor measuring a secondproperty of the media device to generate a second parameter value.Additionally, the example apparatus includes a classifier to combinefirst conditional probabilities of the first parameter value and thesecond parameter value to determine a first state probability for thefirst state of the media device, wherein the first conditionalprobabilities are representative of probabilities the first parametervalue corresponds to the media device being in a first state.Additionally, the classifier combines second conditional probabilitiesof the first parameter value and the second parameter value to determinea second state probability for the second state of the media device,wherein the second conditional probabilities are representative ofprobabilities second parameter value corresponds to the media devicebeing in a second state. Furthermore, the example classifier determinesthe state of the media device by selecting the greater of the firststate probability or the second state probability.

Although the following discloses example apparatus including, amongother components, software executed on hardware, it should be noted thatsuch systems are merely illustrative and should not be considered aslimiting. For example, it is contemplated that any or all of thedisclosed hardware and software components could be embodied exclusivelyin dedicated hardware, exclusively in software, exclusively in firmwareor in some combination of hardware, firmware, and/or software.

The example apparatus and methods to determine the state of a mediadevice using a Naïve Bayes Classifier described herein provide accuratedata to advertisers, media device manufacturers, application developers,and/or producers of media content. The Naïve Bayes Classifier may beincluded within a metering system and/or communicatively coupled to ametering system configured to meter one or more media devices. Themetering data from the metering system generates usage information andprovides an accurate representation of the usage information of personsin metered environments (e.g., individuals, households, etc.).

Generating accurate metering data is difficult because media deviceshave become complex in functionality and interoperability. As a result,media device manufacturers are developing standardized interfaces toease the set-up and connection of these devices (e.g., HDMI-CEC).However, capturing accurate metering information from media devices isstill a challenge. For example, a metered entertainment system mayinclude a digital television, a cable receiver, one or more gamingsystems, a DVD player, a satellite audio receiver, and a connection to apersonal computer with access to the Internet. A metering unitconfigured to detect audio gain signals or visible light may have adifficult time discerning which media device is being used or viewed byan individual and/or household. If the metering device is unable toaccurately determine which devices are used (e.g., the media devicestate), the metered information loses value for the advertisers, themedia device producers, the media device manufacturers, and/or any otherinterested groups.

Media device states may be physical states of a media device or,alternatively, may be application and/or software enabled states of amedia device. Physical states of a media device include states of asingle media device communicatively coupled to one or more mediadevices. For example, a television may be in an OFF state, displayingtelevision programming from a cable receiver, displaying a video gamefrom a gaming system, and/or displaying a movie from a DVD. Furthermore,physical states of a media device may include an ON state, an OFF state,a standby state, a video displaying state, an audio playing state, aconfiguration state, etc. Additionally, media device states may includestates of a single media device with one or more embedded applications.For example, a cellular telephone includes a call state, a textmessaging state, an instant messaging state, an audio listening state, astreaming media content state, a document processing state, and/or a webbrowsing state.

Many methods exist for determining an ON state or an OFF state of asingle media device. These methods utilize sensors associated with ameter and process the data from the sensors within the meter while themeter is located within the metered environment. The sensors metercomplex media devices configurations including media devices with two ormore states. For example, sensors communicatively coupled to a meteringdevice may detect audio signals associated with the operation oftelevisions (e.g., 15.75 kHz signals from the power unit of CRTdisplays), video signals (e.g. light levels), electromagnetic fieldsassociated with a media device and/or remote control signals (e.g.,radio frequency or infrared signals). The sensors send the collecteddata to a metering device that processes the data using one or morealgorithms to determine if the media device is in an ON state or an OFFstate. Currently, many known algorithms are only capable of determininga threshold for the collected data such that data above certainthresholds indicates the media device is in an ON state while data belowthe thresholds indicates the media device is in an OFF state. However,if the device is in an ON state, these known algorithms are not capableof determining the content source providing content for the mediadevice.

The example methods described herein use a Naïve Bayes Classifier todetermine a state of media devices including media devices with two ormore states. The Naïve Bayes Classifier is a probabilistic classifierbased on applying Bayes' theorem with strong naïve independenceassumptions. The Naïve Bayes Classifier is capable of quickly processinga large number of variables independently with a high degree ofclassification accuracy. The Naïve Bayes Classifier enables thedetermination of two or more states from one or more independent datasources.

In order to determine a state of a media device using one or moreinputs, the Naïve Bayes Classifier is trained with a set of trainingexamples. The training examples set a training media device into the oneor more possible states with the Naïve Bayes Classifier recording thecorresponding sensor outputs from sensors monitoring the training mediadevice. By knowing the possible range of sensor outputs (e.g., varianceof outputs) for a given media device state, the Naïve Bayes Classifiercan determine a media device state from similar sensor outputs. In otherwords, the training set provides an estimation of the means andvariances of sensor outputs for each possible state of a metered device.Because the sensor outputs are processed and calculated within a NaïveBayes Classifier independently there is no covariance between the sensoroutputs enabling more accurate state classification.

In the example apparatus described herein the Naïve Bayes Classifier maybe included within a module that processes sensor outputs and includesone or more preliminary classifiers. A preliminary classifier is aclassifier that processes one or more parameters using an algorithm suchas a Fuzzy Logic algorithm that outputs a value used by the Naïve BayesClassifier to determine the state of a media device.

In an example implementation, a Naïve Bayes Classifier may be trained todetect the state of a television. The possible states include atelevision in an ON state, a television displaying content from a gamesystem, a television displaying content provided by a DVD player, or atelevision in an OFF state. The Naïve Bayes Classifier is included in ametering unit that includes a parameter generator to convert sensoroutput signals into normalized digital data and preliminary classifiersto pre-process some of the normalized data.

Prior to use in a metering environment, the Naïve Bayes Classifier istrained by placing a training media device into each of its possiblestates while the Naïve Bayes Classifier records the parameters generatedfrom the sensor outputs to determine the parameter ranges for each inputstate. The training media device is representative of the types of mediadevices the Naïve Bayes Classifier will be configured to meter.Different media devices and/or different types of media device stateshave corresponding training media devices and training sets. The NaïveBayes Classifier calculates probabilities associated with each mediadevice state for each range of parameter values.

Upon training, the metering unit is deployed to a household and/or to amedia device of a panel member. Upon deployment, sensors connected tothe metering unit output sensed information to the parameter generator.The parameter generator converts the sensor outputs to digital data andsends the digital data to the Naïve Bayes Classifier. Additionally, theparameter generator may send some of the digital data to one or morepreliminary classifiers. Upon receiving the digital data from either theparameter generator or the preliminary classifiers, the Naïve BayesClassifier calculates the state of the media device by determining aprobability of each state for each parameter type from the training set,combining the probabilities of the parameters for each state, andselecting the state with the highest probability.

A block diagram of an example metering system 100 capable of providingviewing and metering information for program content presented via anexample media system 102 is illustrated in FIG. 1. The example mediasystem 102 includes a broadcast source 104, a set-top box (STB) 108, asignal splitter 116, a media device 120, and a gaming system 122. Theexample metering system 100 includes a metering unit 124, sensors 144and 146, and a Naïve Bayes Module 128 with a Naïve Bayes Classifier 150.The components of the media system 102 and the metering system 100 maybe connected in any manner including that shown in FIG. 1. For example,in a statistically selected household having one or more media systems102, the metering unit 124 may be implemented as a single home unit andone or more site units. In such a configuration, the single home unitmay perform the functions of storing data and forwarding the stored datato a central facility for subsequent processing. Each site unit iscoupled to a corresponding media system 102 and performs the functionsof collecting viewing/metering data, processing such data (e.g., inreal-time) and sending the processed data to the single home unit forthat home. The home unit receives and stores the data collected by thesite units and subsequently forwards that collected data to the centralfacility.

The broadcast source 104 may be any broadcast media source, such as acable television service provider, a satellite television serviceprovider, a radio frequency (RF) television service provider, aninternet streaming video/audio provider, etc. The broadcast source 104may provide analog and/or digital television signals to the media system102 (e.g., a home entertainment system), for example, over a coaxialcable or via a wireless connection.

The STB 108 may be any set-top box, such as a cable televisionconverter, a direct broadcast satellite (DBS) decoder, a video cassetterecorder (VCR), etc. The set-top box 108 receives a plurality ofbroadcast channels from the broadcast source 104. Typically, the STB 108selects one of the plurality of broadcast channels based on a userinput, and outputs one or more signals received via the selectedbroadcast channel. In the case of an analog signal, the STB 108 tunes toa particular channel to obtain programming delivered on that channel.For a digital signal, the STB 108 may tune to a channel and decodecertain packets of data to obtain programming delivered on a selectedchannel. For example, the STB 108 may tune to a major channel and thenextract a program carried on a minor channel within the major channelvia the decoding process mentioned above. For some media systems 102such as those in which the broadcast source 104 is a standard RF analogtelevision service provider or a basic analog cable television serviceprovider, the STB 108 may not be present as its function is performed bya tuner in the display device 120.

In the illustrated example, an output from the STB 108 is fed to thesignal splitter 116, which may be implemented as a single analogy-splitter (in the case of an RF coaxial connection between the STB 108and the media device 120) or an audio/video splitter (in the case of adirect audio/video connection between the STB 108 and the media device120). For configurations in which the STB 108 is not present, thebroadcast source 104 may be coupled directly to the signal splitter 116.In the example media system 102, the signal splitter 116 produces twosignals indicative of the output from the STB 108. However, any numberof signals may be produced by the signal splitter 116.

In the illustrated example, one of the two signals from the signalsplitter 116 is connected to the media device 120 and the other signalis delivered to the metering unit 124. The media device 120 may be anytype of video display device. For example, the display device 120 may bea television and/or other display device (e.g., a computer monitor, aCRT, an LCD, etc.) that supports the National Television StandardsCommittee (NTSC) standard, the Phase Alternating Line (PAL) standard,the Système Électronique pour Couleur avec Mémoire (SECAM) standard, astandard developed by the Advanced Television Systems Committee (ATSC),such as high definition television (HDTV), a standard developed by theDigital Video Broadcasting (DVB) Project, or may be a multimediacomputer system, etc.

In other example implementations the media device 120 may include or beincorporated as an audio player, a portable video device, a cell phone,a personal digital assistant (PDA), a personal computer, a laptop, a DVDplayer, a portable gaming machine, a satellite radio player, anin-vehicle entertainment system, and/or any other type of device capableof playing audio or displaying media type information.

The media system 102 of FIG. 1 includes the gaming system 122, which iscommunicatively coupled to the media device 120. The gaming system 122may be any type of gaming system 122 capable of providing a userinterface with a video game displayed via the media device 120. Thegaming system 122 may be in an OFF state while the media device 120 isin an ON state (e.g., when a panel member is watching television) or thegaming system 122 and the media device may both be in ON states (e.g.,when a panel member is playing a video game). Furthermore, the gamingsystem 122 and the media device 120 may both be in ON states, but theinput to the media device 120 may be selected such that the media device120 displays information from the broadcast source 104.

In the example of FIG. 1, the second of the two signals from the signalsplitter 116 (i.e., the signal carried by connection 136 in FIG. 1) iscoupled to an input of the metering unit 124. The metering unit 124 is adata logging and processing unit that may be used to generate viewingrecords and other viewing information useful for determining viewing andother metering information. The metering unit 124 typically collects aset of viewing records and transmits the collected viewing records overa connection 140 to a central office or data processing facility (notshown) for further processing and/or analysis. The connection 140 may bea telephone line, a return cable television connection, an RFconnection, a satellite connection, an internet connection, etc.

The metering unit 124 may be configured to determine identifyinginformation based on the signal corresponding to the program contentbeing output by the STB 108. For example, the metering unit 124 may beconfigured to decode an embedded code in the signal received viaconnection 136 that corresponds to the channel or program currentlybeing delivered by the STB 108 for display via the media device 120. Thecode may be embedded for purposes such as, for example, audiencemeasurement, program delivery (e.g., PIDS in a digital televisionpresentation, electronic program guide information, etc.) or delivery ofother services (e.g., embedded hyperlinks to related programming, closedcaption information, etc.). Alternatively or additionally, the meteringunit 124 may be configured to generate a program signature (e.g., aproxy signal that is uniquely representative of the program signal)based on the signal received via connection 136 that corresponds to theprogram currently being delivered by the STB 108 for display via themedia device 120. The metering unit 124 may then add this programidentifying information (e.g., the code(s) and/or signature(s)) to theviewing records corresponding to the currently displayed program.

In the example metering system 100, the Naïve Bayes Module 128 iscoupled to the metering unit 124. Additionally, the Naïve Bayes Module128 is coupled to one or more sensors 144 that are also coupled to themetering unit 124. The sensors 144 may identify and relay information tothe Naïve Bayes Module 128 based on the signal corresponding to theprogram content being output by the STB 108. The sensors 144 may includeone or more sensors configured to monitor data carried by the connection136 or processed within the metering unit 124 for audio signatures,video signatures, programming signatures, programming content, and/orany other type of information transmitted across the splitter 116 fromthe STB 108 to the media device 120.

The Naïve Bayes Module 128 of FIG. 1 includes a Naïve Bayes Classifier150 to process data from the sensors 144 and 146 in order to determinethe state of the media device 120. The Naïve Bayes Classifier 150includes an algorithm implementing Bayes' Theorem to independentlyprocess the outputs from the sensors 144 and 146, to calculate theprobabilities of the media device 120 being in each possible state basedon the outputs from the sensors 144 and 146, and to determine the stateof the media device 120 by selecting the state with the highestprobability of being active.

Information concerning the operating state of the media device 120 istransmitted from the Naïve Bayes Module 128 to the metering unit 124 sothat the metering unit 124 can more accurately process the viewinginformation and viewing records. For example, in the media system 102,it is possible that the media device 120 is turned ON while the STB 108may be inadvertently or intentionally left in an ON (active) state suchthat the STB 108 continues to receive and output program contentprovided by the broadcast source 104. Additionally, the gaming system122 is turned ON. In this example, a panel member may be playing thegaming system 122 using the media device 120 as a display while themetering unit 124 is storing programming information from the STB 108.Without the Naïve Bayes Module 128 the metering unit 124 would indicateto a central office that television programming was viewed during thetime period in which the gaming system 122 was actually used. The NaïveBayes Module 128 corrects this information by using the data from thesensors 144 and 146 to correctly determine the media device 120 is in agaming state and sends this information to the metering unit 124 so thatthe viewing records can be corrected.

The sensors 146 of FIG. 1 may be implemented by any combination of oneor more of an audio sensor, a light sensor, a radio frequency sensor, aninfrared sensor, a motion sensor, a vibration sensor, an angular ratesensor, an acceleration sensor, a pressure sensor, a voltage meter, acurrent meter, a power meter, a thermometer, a magnetic sensor, achemical sensor, and/or any type of device for sensing electrical and/ormechanical properties of a media device. For example, an audio sensor isimplemented by a microphone placed in the proximity of the media device120 to receive audio signals corresponding to the program beingpresented. The Naïve Bayes Module 128 may then process the audio signalsreceived from a microphone (e.g., implementing sensor(s) 144) to decodeany embedded ancillary code(s) and/or generate one or more audiosignatures corresponding to a program being presented. The Naïve BayesModule 128 may also process the audio signal to determine if the mediadevice 120 is in a state associated with emitting audio signals.

Additionally or alternatively, the sensors 146 may be implemented by anon-screen display detector for capturing images displayed via the mediadevice 120 and processing regions of interest in the displayed image.The regions of interest may correspond, for example, to a broadcastchannel associated with the currently displayed program, a broadcasttime associated with the currently displayed program, a viewing timeassociated with the currently displayed program, etc. Furthermore, thesensors 146 may be implemented by a frequency detector to determine, forexample, the channel to which the media device 120 is tuned.Additionally or alternatively, the sensors 146 could be implemented byan electromagnetic (EM) field pickup, a current sensor, and/or atemperature sensor configured to detect emissions from the media device120 indicative of the media device 120 being in an active state.Additionally, the sensors 146 may include any variety of sensors 146that may be coupled to the Naïve Bayes Module 128 to facilitate thedetermination of the state of the media device 120. Any or all of thesensors 146 may be located separate from and/or disposed in the NaïveBayes Module 128 while any or all of the sensors 144 may be locatedseparate from and/or embedded in the metering unit 124. Additionally oralternatively, any or all of the sensors 146 may be duplicated in theNaïve Bayes Module 128 to, for example, facilitate flexible placement ofthe various components of the local metering system 100 to permitmetering of a wide range of media systems 102.

The metering unit 124 and the Naïve Bayes Module 128 may be implementedas separate devices or integrated in a single unit. Additionally oralternatively, any or all or the metering unit 124, the Naïve BayesModule 128, or portions thereof may be integrated into the STB 108and/or the media device 120. For example, Naïve Bayes Module 128 couldbe integrated into the STB 108 such that STB 108 is able to determinewhether program content being received and output is also beingpresented by the monitored media device 120 or corresponding informationpresenting device. Such display device operating state informationcoupled with operating state information concerning the STB 108 itself,can be transmitted back to the broadcast provider responsible for thebroadcast source 104 via a back-channel connection 168 to enable thebroadcast provider to, for example, monitor consumption of programcontent output by the STB 108 and presented by the media device 120 inthe absence of the metering unit 124.

In another example implementation, the Naïve Bayes Module 128, themetering unit 124, and sensors 144 and/or 146 may be embedded within themedia device 120. In such an implementation, the media device 120 mayinclude and/or be implemented as a cellular telephone, a laptop, and/ora PDA. In this example implementation, the Naïve Bayes Module 128determines the state of the media device, where the state of the mediadevice may be an application state such as, for example, a textmessaging application, an audio application, or a calling application.In this case, the sensors 144 and/or 146 may be application adaptersconfigured to gather information upon the activation of respectiveapplications. The sensors 144 and/or 146 may be embedded within thesoftware of the media device 120 or alternatively, may monitorcommunication busses or application specific integrated circuits forinformation regarding the activation and execution of the monitoredapplications.

While an example manner of implementing the metering system 100 isdepicted in FIG. 1, one or more of the interfaces, data structures,elements, processes and/or devices illustrated in FIG. 1 may becombined, divided, rearranged, omitted, eliminated and/or implemented inany other way. For example, the example metering unit 124, the examplesensors 144, the example sensors 146, the example Naïve Bayes Module128, and/or the example Naïve Bayes Classifier 150 illustrated in FIG. 1may be implemented separately and/or in any combination using, forexample, machine accessible instructions executed by one or morecomputing devices and/or computing platforms (e.g., the exampleprocessing platform 1000 of FIG. 10). Further, the example metering unit124, the example sensors 144, the example sensors 146, the example NaïveBayes Module 128, the example Naïve Bayes Classifier 150, and/or, moregenerally, the metering system 100 may be implemented by hardware,software, firmware and/or any combination of hardware, software and/orfirmware. Thus, for example, any of the example Naïve Bayes Module 128,the example Naïve Bayes Classifier 150, and/or more generally, theexample metering system 100 can be implemented by one or morecircuit(s), programmable processor(s), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)), etc. When any of the appendedclaims are read to cover a purely software or firmware implementation,at least one of the example metering unit 124, the example sensors 144,example sensors 146, the example Naïve Bayes Module 128, and/or theexample Naïve Bayes Classifier 150 are hereby expressly defined toinclude a tangible medium such as a memory, DVD, CD, etc. storing suchsoftware or firmware. Further still, the example metering system 100 mayinclude additional devices, servers, systems, networks, gateways,portals, and/or processors in addition to, or instead of, thoseillustrated in FIG. 1 and/or may include more than one of any or all ofthe illustrated devices, servers, networks, systems, gateways, portals,and/or processors.

FIG. 2 is a block diagram showing the example Naïve Bayes Module 128 ofFIG. 1 including an input collector 202, a parameter generator 204,preliminary classifiers 206, a training data memory 208, a transmitter210, and the Naïve Bayes Classifier 150 of FIG. 1. The Naïve BayesModule 128 receives sensor outputs via a connector or input 244 andtransmits a state of a media device to the metering unit 124 of FIG. 1via a connector or output 254. Additionally, the Naïve Bayes Module 128communicates with a training server 233 during training via acommunication path 234. The training communication occurs prior todeployment of the Naïve Bayes Module 128 to a household.

The input collector 202 receives sensor outputs from the sensors 144and/or 146 of FIG. 1. While a single connector or input 244 is shown inFIG. 2, other implementations may include a connector or inputs for eachsensor and/or a bus connector to two or more sensors. Alternatively, theconnector or input 244 may be a wireless connector such as, for example,a Bluetooth™ wireless connection. The connector or input 244 may be anytype of connection to communicatively couple the input collector 202 tothe sensors 144 and/or 146.

The input collector 202 continually receives sensor outputs and samplesthe outputs periodically using a time period specified by the NaïveBayes Classifier 150, the metering unit 124, a third party meteringservice, and/or a system designer (e.g., a person that designed theNaïve Bayes Module 128). For example, the input collector 202 receivesinputs from an audio sensor, a video sensor, an RF sensor, and a lightsensor. The sensors may output digital signals and/or analog signals atdifferent intervals. The input collector 202 receives the outputs fromthe sensors and stores information associated with the sensor outputsuntil a specified time period has elapsed. Alternatively, a sample maybe requested from the parameter generator 204 and/or the Naïve BayesClassifier 150. The collection and storage of sensor outputs by theinput collector 202 ensures the parameter generator 204 receives sensoroutputs at the same time from all sensors monitoring a media device.

The input collector 202 of FIG. 2 may filter the sensor outputs fornoise and/or any other type of electrical interference that masks orreduces the resolution of an electrical signal. The filter may includehardware such as, for example a bandpass filter for filtering audiooutput signals from a microphone or software filters such as, forexample, a filter to extract certain digital bits regarding RF signalstrength from a digital output from an RF sensor. Additionally, theinput collector 202 may separate a single sensor output into two or moresignals for the parameter generator 204. For example, the inputcollector 202 may receive an audio signal from a microphone via a singleconnection. The input collector 202 may then split the audio signal intothree signals corresponding to audio gain, audio average energy, andaudio signatures, each of which the parameter generator 204 may processindividually. The splitting of the signal may occur before signalfiltering or after signal filtering.

The parameter generator 204 receives sensor outputs via the inputcollector 202 and processes the sensor outputs to generate parametersfor the preliminary classifiers 206 and/or the Naïve Bayes Classifier150. The parameter generator 204 processes sensor outputs by convertingthe analog and/or digital signal(s) into digital value(s) interpretableby the Bayes algorithm in the Naïve Bayes Classifier 150 and/or by otheralgorithms in the preliminary classifiers 206. The processing mayinclude converting an analog signal into a digital signal and thennormalizing the digital signal. Additionally, the parameter generator204 may receive a digital signal from the input collector 202, interruptdata bits within the digital signal and normalize the data within thedigital signal.

The normalization of digital information creates consistency of inputparameters for the Naïve Bayes Classifier 150. For example, audio gainmay normally be expressed in decibels, current may be expressed inmilliamps (mAs), while IR frequency may be expressed in kilohertz (kHz).The Naïve Bayes Classifier 150 is capable of independently processingthe parameters with un-normalized data from multiple sensor outputs.However, the calculations are simplified when the data is normalized.Additionally, normalization reduces resolution of sensor outputs incases where the sensor output may have a high resolution. However, therange of values for each state does not require the same highresolution. Resolution reduction via normalization may decreaseprocessing time and/or load by processing data at lower resolutions. Forexample, a processer is capable of processing 6 bits of data faster thanprocessing 12 bits representing the same data.

In an example implementation, a part of the parameter generator 204 isconfigured to process audio signals from the monitored media device 120and detected by an audio sensor. The parameter generator 204 includes ananalog-to-digital (A/D) converter to convert the audio signals to adigital format for processing. The parameter generator 204 also includesa variable gain amplifier (VGA) that may amplify or attenuate, asneeded, the audio signals so that the audio signals appropriately fillthe dynamic range of the A/D converter to yield a desired bit resolutionat the output of the A/D converter.

The parameter generator 204 may be configured to control thegain/attenuation provided by the VGA based on any known automatic gaincontrol (AGC) algorithm. For example, an AGC algorithm implemented bythe parameter generator 204 may control the VGA to yield an output ofthe A/D converter having an amplitude, variance, standard deviation,energy, etc. within a predetermined range. The predetermined range istypically derived from the characteristics of the particular A/Dconverter to result in a gain/attenuation of the VGA that appropriatelyfills the dynamic range of the A/D converter.

The Naïve Bayes Module 128 of FIG. 2 includes a Naïve Bayes Classifier150 to process parameters from the parameter generator 204 to determinethe state of the media device 120. The Naïve Bayes Classifier 150includes an algorithm implementing Bayes' Theorem to independentlyprocess the outputs from the sensors 144 and 146 to calculate theprobabilities of the media device 120 being in each possible state basedon the outputs from the sensors 144 and 146, and to determine the stateof the media device 120 by selecting the state with the highestprobability of being active.

The Naïve Bayes Classifier 150 is a probabilistic classifier based onapplying Bayes' theorem with strong naïve independence assumptions. TheNaïve Bayes Classifier 150 is capable of quickly processing a largenumber of variables independently with a high degree of classificationaccuracy. The intrinsic nature of the Naïve Bayes Classifier 150 enablesthe determination of two or more states from one or more independentdata sources. This enables the Naïve Bayes Classifier 150 to determine astate of a media device from a plurality of states and for a variety ofdifferent types of media devices and/or for different types ofapplications.

The accuracy of the Naïve Bayes Classifier 150 is due in part to thetraining of the Naïve Bayes Classifier 150 prior to implementation in ametering system. The training involves exposing the Naïve BayesClassifier 150 to a training set of known media device states andrecording sensor outputs corresponding to each state. In a trainingroutine the state of the media device is sent to the Naïve BayesClassifier 150 via the communication path 234. Additionally, the NaïveBayes Classifier 150 receives processed and normalized sensor outputs asparameters from the parameter generator 204. The parameter values areprocessed by the Naïve Bayes Classifier 150 and noted for each mediadevice state. This enables the Naïve Bayes Classifier 150 to formulate arange of parameter values and/or likely parameter values for each state.Furthermore, the Naïve Bayes Classifier 150 may assign weights to eachrange of parameter values for each media device state. In other exampleimplementations the Naïve Bayes Classifier 150 may determine arelationship expressed as an equation to calculate a conditionalprobability and/or weight based on received parameter values.

The training routine determines a probability distribution of theconditional probabilities for each of the independent parameters foreach media device state. The distribution may be partitioned into rangesof values corresponding to the frequency of certain parameter values.These conditional probabilities are used by the Naïve Bayes Classifier150 to calculate the probability of a media device in each of itspossible states in order to determine the state of the metered mediadevice. Alternatively, the Naïve Bayes Classifier 150 may calculateweights for each of the independent parameters for each media devicestate. The calculated conditional probabilities or weights are stored bythe Naïve Bayes Classifier 150 in the training data memory 208. Inalternative implementations, an external server or processor may processthe training set and store the training probabilities or odds in thetraining data memory 208 via the communication path 234. A trainingexample for an example implementation of the Naïve Bayes Classifier 150is shown in FIG. 4 and example training weights are shown in FIG. 5.

The training data memory 208 of FIG. 2 stores conditional probabilitiesand/or weights determined from a training routine. The storedconditional probabilities and/or weights are accessed by the Naïve BayesClassifier 150 to determine the state of a media device. The trainingdata memory may include a read-only memory (ROM), a random access memory(RAM), and/or any other type of memory. The RAM may be implemented by,for example, dynamic random-access memory (DRAM), synchronous dynamicrandom-access memory (SDRAM), and/or any other type of RAM device(s).The ROM may be implemented by, for example, flash memory(-ies) and/orany other desired type of memory device(s). Access to the training datamemory may be controlled by a memory controller (not shown).

The Naïve Bayes Classifier 150 may calculate the state of a media deviceusing any Naïve Bayes algorithm with Laplace smoothing that usesconditional probabilities and/or weights of independent parameterscombined together to determine the probability and/or odds of a mediadevice being in a state. Additionally, the Naïve Bayes Classifier 150may use any Naïve Bayes algorithm to select the highest probability orgreatest odds of all the calculated states to determine the most likelystate of the media device.

In an example, the Naïve Bayes Classifier 150 implements Bayes' Theoremexpressed below in equation (1). The example assumes a sample space,which is partitioned into a number of subspaces. In other words, thesample space is a media device and the number of subspaces is the totalnumber of possible states in the media device. Each state is designatedby X_(i), where X₁ may be an ON state, X₂ may be an OFF state, X₃ may bea GAME state, X₄ may be a DVD state, etc. If the Naïve Bayes Module 128monitors a media device in an unknown state with certain knownsensor-measured parameters, which are in a sample space Y that includesthe entire set of observed values for the parameters, the probabilitythat the media device is in a particular state X_(i) is given byequation (1):

$\begin{matrix}{{P\left\lbrack X_{i} \middle| Y \right\rbrack} = \frac{{P\left\lbrack Y \middle| X_{i} \right\rbrack} \cdot {P\left\lbrack X_{i} \right\rbrack}}{\sum\limits_{j}{{P\left\lbrack Y \middle| X_{j} \right\rbrack} \cdot {P\left\lbrack X_{j} \right\rbrack}}}} & (1)\end{matrix}$

In this equation, the denominator is independent of the media devicestate and is expressed as the sum of the probabilities of the entire setof observed values Y. The numerator is calculated by multiplying theconditional probabilities of each sensor parameter for the entire set ofobserved values Y and the probability of the particular state Xi.

For example, three parameters are measured by sensors for a media devicein a unknown state. To determine the probability of the state being theX₃ state, the conditional probabilities for the parameters given stateX₃ are loaded from the training data memory 208 for the measuredparameter values. If the conditional probability of the audio gainmeasured for state is 70%, the conditional probability of the averageaudio energy measured for the state is 30%, and the conditionalprobability of the visible light measured for state is 50%, then thenumerator would be the product of the three probabilities and theprobability of the state itself. Then, in this example, the probabilitythe media device is in state X₃ is the product of the conditionalprobabilities divided by the probability of the entire known set Y. Theexample is repeated for the other states of the media device. Uponcalculating the probability of all states, the Naïve Bayes Classifier150 determines the media device state as the state with the greatestprobability.

In another example implementation, equation (1) may be modified tocalculate the natural logarithmic odds for each media state. In thisexample, the odds the media device is in a certain state are determinedby taking the sum of the log of the conditional probabilities of all theparameters for one state, adding the log of the probability of the stateitself, and subtracting the log of the probability of the entire knownset Y. The state of the media device is then determined by the NaïveBayes Classifier 150 as the sate with the greatest logarithmic oddsvalue. In yet other examples, the equation (1) may not divide by theentire set of observed values Y if Y is common for all possible mediadevice states.

The Naïve Bayes Classifier 150 of FIG. 2 sends the determined state of amedia device to the transmitter 210. The transmitter 210 then transmitsthe state of the media device to the metering unit 124 of FIG. 1. Inother example implementations the transmitter 210 may send the state ofa metered media device to central office or data processing facility forfurther processing or analysis. Furthermore, the transmitter 210 maysend the media device state to a memory for storing the state of a mediadevice in the Naïve Bayes Module 128. The transmitter 210 may includeany type of transmitting hardware and/or machine readable mediumconfigured to send media device state information via any type ofcommunication protocol to an appropriate receiver.

The Naïve Bayes Module 128 may include a single preliminary classifier206 or multiple types of preliminary classifiers 206. Alternatively, insome cases, the Naïve Bayes Module 128 may not include the preliminaryclassifiers 206. The preliminary classifiers 206 implement algorithmsused by the Naïve Bayes Module 128 to preprocess parameters from theparameter generator 204. The processed output from the preliminaryclassifiers 206 is sent to the Naïve Bayes Classifier 150. Thepreliminary classifiers 206 may include a fuzzy logic algorithm, aneural network algorithm, a decision tree algorithm, a rule indicationalgorithm, and/or an instance-based algorithm.

The preprocessing by the preliminary classifiers 206 may include adecision as to whether the media device is in a certain state and/or aconfidence level indicating a media device is in a certain state.Additionally, the preprocessing by the preliminary classifiers 206 mayprovide better resolution of some parameter data. Furthermore, thepreliminary classifiers 206 may be configured to process some types ofinterdependent and/or covariant parameter data. For example, a fuzzylogic algorithm used as preliminary classifier 206 may calculate aconfidence of a media device state by processing parameter values foraverage audio gain and average audio energy together. The preliminaryclassifier 206 then processes the combined parameter value in the fuzzylogic decision based algorithm and outputs a confidence value or anormalized value indicating a media device state. The Naïve BayesClassifier 150 then receives the output and processes the dataindependently with other parameters from the parameter generator.

While an example manner of implementing the Naïve Bayes Module 128 isdepicted in FIG. 2, one or more of the interfaces, data structures,elements, processes and/or devices illustrated in FIG. 2 may becombined, divided, rearranged, omitted, eliminated and/or implemented inany other way. For example, the example input collector 202, the exampleparameter generator 204, the example preliminary classifiers 206, theexample Naïve Bayes Classifier 150, and/or the example transmitter 210illustrated in FIG. 2 may be implemented separately and/or in anycombination using, for example, machine accessible instructions executedby one or more computing devices and/or computing platforms (e.g., theexample processing platform 1000 of FIG. 10). Further, the example inputcollector 202, the example parameter generator 204, the examplepreliminary classifiers 206, the example Naïve Bayes Classifier 150, theexample transmitter 210, and/or more generally, the Naïve Bayes Module128 may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example input collector 202, the example parameter generator204, example preliminary classifiers 206, the example Naïve BayesClassifier 150, the example transmitter 210, and/or more generally, theNaïve Bayes Module 128 can be implemented by one or more circuit(s),programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)), etc. When any of the appendedclaims are read to cover a purely software or firmware implementation,at least one of the example input collector 202, the example parametergenerator 204, example preliminary classifiers 206, the example NaïveBayes Classifier 150, and/or the example transmitter 210 are herebyexpressly defined to include a tangible medium such as a memory, DVD,CD, etc. storing such software or firmware. Further still, the exampleNaïve Bayes Module 128 may include additional devices, servers, systems,networks, gateways, portals, and/or processors in addition to, orinstead of, those illustrated in FIG. 2 and/or may include more than oneof any or all of the illustrated devices, servers, networks, systems,gateways, portals, and/or processors.

FIG. 3 is a block diagram showing an example of the Naïve Bayes Module128 of FIG. 2 including the sensors 144 and parameters 322-338. In theexample described herein, the Naïve Bayes Module 128 meters the state ofthe media device 120 of FIG. 1. The media device 120 may include anytype of media device described in conjunction with the media device 120in FIG. 1. Additionally, the parameter generator 204, the preliminaryclassifiers 206, and the Naïve Bayes Classifier 150 are similar oridentical to the like numbered elements of FIG. 2. Specifically, theexample shown in FIG. 3 includes an audio sensor 144 a, a video sensor144 b, a current sensor 144 c, an IR sensor 144 d, and an AM radiosensor 144 e. Furthermore, the preliminary classifiers 206 include afuzzy logic classifier 206 a and a neural network classifier 206 b. Theinput collector 202, the transmitter 210, and the training data memory208 from FIG. 2 are not shown.

The sensors 144 monitor certain electromagnetic properties correspondingto the media device 120 and convert these properties into electricalsignals that are provided to the parameter generator 204. For example,the audio sensor 144 a may be implemented as a microphone thattransduces audio signals into corresponding electrical signals. Thevideo sensor 144 b may be implemented as a camera or light detectiondevice that transduces light waves into corresponding electricalsignals. The current sensor 144 c may be implemented as a currentmeasuring device coupled to a power cord of the media device 120 tomeasure amps of current and/or power drawn by the media device 120. TheIR sensor 144 d detects infrared energy emitted by the media device 120and/or peripherals associated with the media device 120, and the AMradio sensor 144 e transduces AM radio signals into correspondingelectrical signals. The sensors 144 may transmit electrical informationto the Naïve Bayes Module 128 via digital signals and/or analog signals.Additionally, the electrical information may be transmitted via awireless connection or, alternatively, via a wired connection. In otherexamples, the sensors 144 may be embedded within the media device 120and may utilize hardware and/or software within the media device 120.

The output properties received from the sensors 144 by the parametergenerator 204 may be filtered for noise and/or any other types ofelectrical interference. The filters may include gain amplifiers,analog-to-digital converters, capacitors, and/or any other types ofelectrical component that may affect signal strength, signal quality, orinformation included within the signal. Some sensor outputs are thensplit into multiple connections for processing different types ofparameter information.

For example, the output from the audio sensor 144 a is split into threesignals. The first signal is for determining the audio gain parameter322, the second signal is for determining the average audio energyparameter 324, and the third signal is for determining the audiosignatures parameter 326. The audio signal from the audio sensor 144 ais divided into the three parameters 322-326 so that the parametergenerator 204 can process the same audio signal from the audio sensor144 a differently. For example, the processing and calculation of valuesfor the audio gain parameter is different from the calculation andprocessing of audio signatures. The gain parameter may be calculated bydetermining the decibel gain level of the analog audio signal whileaudio signatures are determined from certain patterns of the analogaudio signal.

In the same manner, the video sensor 144 b output signal is split intotwo signals used to determine the visible light parameter 328 and thevideo frames parameter 330, respectively. Likewise, the IR sensor 144 doutput is divided into two signals to determine the IR frequencyparameter 334 and the IR average energy parameter 336. Upon splittingsensor output properties or signals, the parameter generator 204calculates, processes, and normalizes the data into the parametervalues. The parameter values are then transmitted from the parametergenerator 204 to either the preliminary classifiers 206 or the NaïveBayes Classifier 150. In the example of FIG. 3, the audio signatureparameter 326 and the visible light parameter 328 are sent to the fuzzylogic classifier 206 a, and the video frames parameter 330 and the IRfrequency parameter are sent to the neural network classifier 206 b. Thegain parameter 322, the average audio energy parameter 324, the currentparameter 332, the IR average energy parameter 336, and the AM RFaverage energy parameter 338 are conveyed via a bus 340 directly to theNaïve Bayes Classifier 150.

The fuzzy logic classifier 206 a of FIG. 3 processes the audio signatureparameter 326 values and the visible light parameter 328 values via afuzzy logic algorithm. For example, the fuzzy logic algorithm may uselinguistic processors to calculate a value from digital informationwithin the audio signature parameter 326 value and combine thatinformation with the visible light parameter 328 value. In other words,audio signatures determined from the audio signal such as, for examplekey words, names, phrases, and/or noises emitted from the media device120 may be processed by the fuzzy logic algorithm to determine aprocessed value corresponding to a state of the media device. Phrasescommon to commercials may indicate the media device is in a televisionstate while the lack of commercials may indicate a gaming state or amovie watching state. This value is then combined with the visible lightparameter 328 value. The combined value processed by the fuzzy logicalgorithm may indicate if the media device 120 is in a television state,a game playing state, or a DVD playing state. Upon processing theparameter values 326 and 328, the fuzzy logic classifier 206 a sends anoutput parameter value to the Naïve Bayes Classifier 150. The outputparameter value may be a normalized numerical value or may be any otherdigital and/or analog type value capable of being processed by the NaïveBayes Classifier 150.

The neural network classifier 206 b processes the video frames parameter330 value and the IR frequency parameter 334 value via a neural networkalgorithm. For example, the neural network algorithm may be arranged ina cascading formation such that video frame information may be processedbased on the types of information within the video images. The videoimage information may be combined with IR frequency information todetermine information about the state of the media device.

The video images may be processed through the neural network such thatthe video images are partitioned into different sections. Then, theneural network algorithm matches the different sections of the videoimages to certain neural network functions represented as image clips.As certain video sections match image clips more video sections arecompared to additional clips. The progression of matching images may bespecified by the cascading nature of the neural network. A value isdetermined from the number of matching video image sections to imageclips. For example, certain video sections may correspond to images froma video game. The neural network classifier 206 b matches the videosections to image clips and determines a confidence value of themonitored media device in a gaming state. Furthermore, the IR frequencymay be processed through the neural network and combined with theprocessed video clip value. This combined output parameter value is thentransmitted to the Naïve Bayes Classifier 150. The output parametervalue may be a normalized numerical value or, alternatively, any otherdigital and/or analog type value capable of being processed by the NaïveBayes Classifier 150.

The Naïve Bayes Classifier 150 of FIG. 2 receives parameters frompreliminary classifiers 206 and/or the parameter generator 204 via thebus 340. The Naïve Bayes Classifier 150 calculates the state of a mediadevice using any Naïve Bayes algorithm with Laplace smoothing that usesconditional probabilities and/or weights of independent parameterscombined together to determine the probability and/or odds of a mediadevice being in a state. Additionally, the Naïve Bayes Classifier 150may use any Naïve Bayes algorithm to select the highest probability orgreatest odds of all the calculated states to determine the most likelystate of the media device.

For each parameter value received by the Naïve Bayes Classifier 150, theNaïve Bayes Classifier 150 loads the conditional probabilities for eachpossible state for each parameter value. For example, if the Naïve BayesClassifier 150 receives an audio gain parameter 322 value of 5.6measured for a media device with three possible states, ON, OFF, andGAMING. The 5.6 value corresponds to a 45% conditional probability forthe state ON, a 57% conditional probability for the state GAMING, and a22% conditional probability for the state OFF. The Naïve BayesClassifier 150 then calculates the probability for each of the threestates (e.g., ON, OFF, and GAMING) by combining all of the conditionalprobabilities for the ON state for all of the parameters 322-338, thencombining all of the conditional probabilities for the OFF state for allof the parameters 322-338, and finally combining all of the conditionalprobabilities for the GAMING state for all of the parameters 322-338. Adescription of the calculations for combining the conditionalprobabilities is described above in conjunction with FIG. 2.

Upon calculating the probabilities for each state, the Naïve BayesClassifier 150 of FIG. 3 selects the media device state corresponding tothe greatest probability. The Naïve Bayes Classifier 150 then sends themedia device state via output 342 to the metering unit 124 of FIG. 1. Inother example implementations, the Naïve Bayes Classifier 150 may sendthe media device state to a third party collecting media deviceinformation, and/or to a memory for storing media device information.

FIG. 4 shows a table including an example training set or data 400 forthe example Naïve Bayes Module 128 of FIG. 2. The training set 400includes the parameters 322-338 of FIG. 3 and a parameter for the stateof the media device (i.e., STATE). The first ten rows of the trainingset 400 correspond to recorded parameter values for a media device in anOFF state, the next ten rows correspond to parameter values for a mediadevice in a television mode (i.e., TV ON), and the last ten rowscorrespond to parameter values for a media device in a gaming state(i.e., GAME ON). The parameter values may differ for a media device inone state due to the different possible levels of the state. Forexample, a media device in an ON state may have different volumesettings, different contrast settings, etc. For simplicity and brevity,the example of FIG. 4 includes parameter values normalized to a valuebetween 0.0-2.0 and the example does not include preliminaryclassifiers. In other examples, the parameters may include any analog ordigital numerical values, any normalized or non-normalized values,and/or outputs from one or more preliminary classifiers.

The training set 400 of FIG. 4 may be determined by placing a trainingmedia device in one of the three states and recording the parametersfrom sensors monitoring the media device. The known state of the mediadevice is then associated with the parameter values associated with thestate. In this case, the media device may be representative of a mediadevice in a household the Naïve Bayes Module 128 is to meter withtraining sensors representative of sensors used to record electricaloutputs from the media device.

In other examples, the training set 400 may represent simulatedparameter values and the corresponding media device states. Thesimulated parameters may be determined from a collection of parametervalues collected by one or more metering units and/or Naïve BayesModules 128, or by calculations of sensor outputs for different mediadevice states. For the simulated parameter values, the training set 400is processed in the same manner as if the parameter values were fromsensors monitoring a media device.

FIG. 5 is a table 500 showing trained weights for the example NaïveBayes Module 128 of FIG. 2 derived from the training set 400 of FIG. 4.The parameters 322-338 are shown with normalized ranges of parametervalues for each media device state. For simplicity and brevity thenormalized values for each parameter are organized into three ranges(i.e., 0.0, 1.0, and 2.0). In other examples, different parameters mayhave a different number of ranges, with each range for different amountsof values.

The table 500 is calculated from the training set 400 of FIG. 4 bycounting the number of instances of each parameter value for eachparameter in each media device state. For example, the gain parameterand the OFF state has two instances of 0.0, eight instances of 1.0, andzero instances of 2.0 in the training set 400. The last row, TOTALINSTANCES, shows the total number of training instances for each mediadevice state. The Naïve Bayes Classifier 150 may process the trainingset 400 to generate the table 500. Alternatively, a server and/orcomputer external to the Naïve Bayes Module 128 may process the trainingset 400 to generate the table 500. The table 500 is stored in thetraining data memory 208 of FIG. 2.

The numbers of instances are reflected as weights in table 500. In someexamples, the number of instances may be divided by the total number ofpossible instances for a media device state (e.g., 10) to calculate aconditional probability. Thus, the weight of 2 in the OFF state of the0.0 gain parameter value has a conditional probability value of 20%. Inyet other examples, the weights and/or conditional probabilities may beexpressed as an equation of the parameter value. For example, the OFFstate conditional probability of visible light may be equated to(25x²−10x)/100, where x is the visible light parameter value. Thus, if xis 2, then the conditional probability is 80%.

The Naïve Bayes Classifier 150 uses the instances shown in the table 500to calculate the media device state probabilities. The instancesrepresent the likelihood a media device is in a certain state based on aparameter. For example, the Naïve Bayes Classifier 150 receives a gainparameter value of 1.0. The Naïve Bayes Classifier 150 uses thecorresponding weight of 8 to calculate the probability of the mediadevice being in the OFF state, the weight of 3 to calculate theprobability of the media device being in the TV ON state, and the weightof 6 to calculate the probability of the media device being in the GAMEstate. The weights may be converted into conditional probabilities bydividing each weight by the total number of possible instances for themedia device state (e.g., 10). Thus, there is an 80% probability of again parameter value of 1.0 if the media device is in an OFF state, a30% probability of a gain parameter value of 1.0 if the media device isin a TV ON state, and a 60% probability of a gain parameter value of 1.0if the media device is in a GAME state.

In other examples, the Naïve Bayes Classifier 150 may use Laplacesmoothing to avoid calculating the probability of the state of a mediadevice using conditional probabilities with a value of 0%. In somealgorithms a 0% conditional probability value may decrease the accuracyof the calculations. In an example of Laplace smoothing, a gainparameter value of 0.0 corresponds to a weight of 0 for the TV ON state.The Naïve Bayes Classifier 150 may use Laplace smoothing for calculatingthe conditional probability for the TV ON state given a 0.0 gainparameter value. Instead of dividing the weight by the total number ofinstances (e.g., 10), the conditional probability is calculated byadding a value of 1 to the weight then dividing by the total number ofinstances plus the total number of possible states (e.g., 3). As aresult, the conditional probability is 7.7%, instead of 0% withoutLaplace smoothing. Likewise, conditional probabilities may be calculatedfor the other weights shown in table 500 using Laplace smoothingregardless of the value of the weight. In the prior example, the weightof 8 may be converted to a conditional probability of 69% using Laplacesmoothing. In yet other examples, the conditional probability may becalculated by adding a value of 1 to the weight, then dividing theweight by the total number of instances or dividing the weight by thetotal number of instances plus a value.

FIG. 6 is a table 600 showing example parameter values from sensorsmonitoring a media device, corresponding weights from the table 500 ofFIG. 5, and calculated probabilities of the state of the media device.Table 600 shows an example calculation of media device states based onan example set of parameters. In the example of FIG. 6, normalizedparameter values are shown as example inputs for the Naïve BayesClassifier 150.

In the example, the parameter generator 204 sends the Naïve BayesClassifier 150 a gain parameter value of 0.0, an average audio energyparameter value of 2.0, an audio signature parameter value of 2.0, avisible light parameter value of 2.0, a video frame parameter value of2.0, a current parameter value of 1.0, an IR average energy parametervalue of 2.0, an IR frequency parameter value of 2.0, and an AM averageenergy parameter value of 2.0. The Naïve Bayes Classifier 150 accessesthe weights and/or conditional probabilities in the table 500 associatedwith these parameter values. The values are shown for each state for theparameter types in table 600. The Naïve Bayes Classifier 150 thencombines the independent parameter values to determine a media stateconfidence. The confidence value is calculated by converting the weightsto conditional probabilities and multiplying together the conditionalprobabilities for each of the parameter types for each state of themedia device. Then, the natural log is calculated from the resultant ofthe multiplied conditional probabilities and from the log of theprobability of the state itself resulting in the confidence value shownin the table 600. The natural log is used to show the confidenceprobability as a base rather than a very small decimal value. However,in other examples, the Naïve Bayes Classifier 150 may determine thestate of the media device using the decimal value of the confidencevalues.

The table 600 in FIG. 6 shows that the OFF state has the greatest mediastate confidence value. The Naïve Bayes Classifier 150 then transmitsinformation including that the metered media device is in an OFF state.In other example implementations, the Naïve Bayes Classifier 150 maycalculate the probability of each state and select the state with thegreatest probability. In other examples, the table 600 may includeconditional probabilities instead of the parameter weights, where theconditional probabilities may be calculated using Laplace smoothing.

FIGS. 7, 8, and 9 are flowcharts representative of example processesthat may be performed to determine the state of a media device. Theexample processes may be implemented as machine readable instructionsexecuted using, for example, a processor system such as the system 1000of FIG. 10. However, one or more of the blocks depicted in theflowcharts may be implemented in any other manner, including bydedicated purpose circuitry, manual operations, etc. Additionally,although the example processes are described with reference to theflowcharts of FIGS. 7, 8, and 9, other methods to determine the state ofa media device may additionally or alternatively be used. For example,the order of execution of the blocks depicted in the flowcharts of FIGS.7, 8, and 9 may be changed, and/or some of the blocks described may berearranged, eliminated, or combined.

The example process 700 represented by FIG. 7 may be performed toimplement the example Naïve Bayes Module 128 and/or the example NaïveBayes Classifier 150 of FIGS. 1 and 2. The example process 700 may beexecuted at predetermined intervals, based on an occurrence of apredetermined event, in response to a user request, etc., or on anycombination thereof. For example, the process 700 may be executed atpredetermined intervals, such as hourly, daily, etc. Additionally oralternatively, the example process 700 may be executed upon theoccurrence of a trigger generated remotely such as, for example, a thirdparty monitoring company preparing the Naïve Bayes Module 128 fordeployment in a household by initiating a training routine.

The example process 700 of FIG. 7 shows a training routine for the NaïveBayes Module 128 of FIGS. 1 and 2 and begins with the determination ofthe number and types of sensors for monitoring a media device (block702). Additionally, the types of outputs for each sensor are determined.Next, the parameter generator 204 within the Naïve Bayes Modules 128 isconfigured for the corresponding sensor output types (block 704). Theparameter generator 204 is configured so that sensor outputs areconverted into the specified digital format and so that the parametergenerator 204 routes the resulting parameters to a specified preliminaryclassifier or the Naïve Bayes Classifier 150.

The example process 700 continues when the training routine is initiatedfor the Naïve Bayes Module (block 706). The training routine starts bysetting a training media device in a first state (block 708). Thetraining media device is representative of the types of media devicesthe Naïve Bayes Module 128 will meter in a household. Upon setting thetraining media device into the first state, the Naïve Bayes Module 128monitors and stores the parameters converted from the sensor outputs bythe parameter generator 204 (block 710). The parameters include allparameter types output by the parameter generator 204. The parametervalues for the first state are stored as a first set of data. Then, thefirst state of the media device is stored (block 712). The parametersand media device state may be stored within the Naïve Bayes Module 128or, alternatively, the media device state and parameters may be storedon an external server.

Next, a check is performed to determine if the training routine isfinished (block 714). If the training routine is not finished, controlreturns to block 708 where the media device is placed into anotherstate. The media device may be placed into the first state again or thestate may be changed to any one of the other possible states of themedia device. If the training routine is finished, the external serverand/or the Naïve Bayes Module 128 compiles the stored parameters andcorresponding media device states (block 716). Then, the Naïve BayesModule 128 and/or the external sever calculates the conditionalprobabilities (and/or weights) for the parameter values for each mediadevice state (block 718). The conditional probabilities are calculatedby distributing the parameter values for each parameter type and mediadevice state. Different portions of the distribution are assignedprobabilities based on the number instances of a parameter value for amedia device state with consideration to the total number of instances.

For example, a training routine may include 10 instances of a mediadevice in a game state. A trained parameter includes a normalized audiogain parameter with a potential range of values between 0 and 10. In thetraining routine, the audio gain parameter has zero instances betweenvalues of 0 to 3, seven instances of values between 3 to 5, twoinstances of values between 5 to 7 and one instance of a value between 7and 10. The conditional probability is the number of instances of arange divided by the total number of instances. Thus, the normalizedaudio gain for the range 3 to 5 has a conditional probability of 70%(e.g., seven instances out of ten possible instances). In other words,if the Naïve Bayes Classifier 150 receives an average audio gain between3 and 5, the conditional probability is 70%, which is used to calculatethe total probability of the media device in the game state.

In other example implementations the distribution may be split bysmaller numerical ranges, by larger numerical ranges, or by singlevalues. In yet other example implementations the server and/or the NaïveBayes Module 128 may determine an equation relating parameter values toconditional probabilities. The example process 700 ends when the trainedconditional probabilities (and/or weights) or equations for calculatingconditional probabilities from parameter values are stored into thetraining data memory 208 of the Naïve Bayes Module 128.

The example process 800 represented by FIG. 8 may be performed toimplement the example Naïve Bayes Module 128 and/or the example NaïveBayes Classifier 150 of FIGS. 1 and 2. The example process 800 may beexecuted at predetermined intervals, based on an occurrence of apredetermined event, in response to a user request, etc., or on anycombination thereof. For example, the process 800 may be executed atpredetermined intervals, such as hourly, daily, etc. Additionally oralternatively, the example process 800 may be executed upon theoccurrence of a trigger generated remotely such as, for example, theactivation of the Naïve Bayes Module 128 to monitor a media device.

The example process 800 of FIG. 8 begins when the Naïve Bayes Module128, coupled to a metering unit monitoring a media device, receivesoutputs from sensors monitoring the media device (block 802). Uponreceiving the sensor outputs, the parameter generator 204 processes theoutputs by filtering, attenuating, splitting, and/or converting theoutput properties into parameters (block 804). Next, the parametergenerator 204 determines if any of the parameter values are to benormalized (block 806). For the parameters that are specified to benormalized, the parameter generator 204 normalizes those parameters(block 808). The normalization standardizes the numerical informationfor the Naïve Bayes Classifier 150. For example, IR frequency expressedin megahertz and audio gain expressed in decibels can both be normalizedfor a linear numerical range from 0 to 10. Upon normalizing theparameters, the parameter generator 204 sends the parameter values tothe Naïve Bayes Classifier 150 (block 810). Furthermore, for parametersthat are not specified to be normalized, the parameter generator sendsthe parameter values to the Naïve Bayes Classifier 150 (block 810).

The example process continues 800 when the Naïve Bayes Classifier 150loads the training data of conditional probabilities and/or weights fromthe training data memory (block 812). Next, the Naïve Bayes Classifier150 analyzes the parameter values and matches the correspondingconditional probabilities for each media device state to each parametervalue (block 814). Then, for each possible state of the media device,the conditional probabilities for each parameter are calculatedresulting in probabilities for each media device state (block 816). Thecalculation may include using equation (1) described in conjunction withFIG. 2 or a sum of logarithmic conditional probabilities of eachparameter value. Next, the Naïve Bayes Classifier 150 selects the statewith the highest probability and/or the greatest log odds (block 818).Then, the example process ends when the Naïve Bayes Module 128 outputsthe state of the media device (block 820).

The example process 900 represented by FIG. 9 may be performed toimplement the example Naïve Bayes Module 128 and/or the example NaïveBayes Classifier 150 of FIGS. 1 and 2. The example process 900 may beexecuted at predetermined intervals, based on an occurrence of apredetermined event, in response to a user request, etc., or on anycombination thereof. For example, the process 900 may be executed atpredetermined intervals, such as hourly, daily, etc. Additionally oralternatively, the example process 900 may be executed upon theoccurrence of a trigger generated remotely such as, for example, theactivation of the Naïve Bayes Module 128 to monitor a media device.

The example process 900 of FIG. 9 begins when the Naïve Bayes Module128, coupled to a metering unit monitoring a media device, receivesoutputs from sensors monitoring the media device (block 902). Uponreceiving the sensor outputs, the parameter generator 204 processes theoutputs by filtering, attenuating, splitting, and/or converting theoutput properties into parameters (block 904). Next, the parametergenerator 204 determines if any of the parameter values are to benormalized (block 906). For the parameters that are to be normalized,the parameter generator 204 normalizes those parameters (block 908). Thenormalization standardizes the numerical information for the Naïve BayesClassifier 150. Upon normalizing the parameter values, the parametergenerator 204 determines if any of the parameters are to be sent to apreliminary classifier (block 910). Furthermore, for parameters that arenot specified to be normalized, the parameter generator determines ifany of the parameters are to be sent to a preliminary classifier (block910).

If one or more parameter values are to be sent to a preliminaryclassifier, the example process 900 sends the one or more parametervalues to the appropriate preliminary classifier (block 910). The NaïveBayes Module 128 may include one or more preliminary classifiers withsome parameters sent to a first preliminary classifier, some parameterssent to a second preliminary classifier, and/or some parameters not sentto a preliminary classifier. Additionally, some parameters may be sentto more than one preliminary classifier. The parameters not sent to apreliminary classifier are transmitted to the Naïve Bayers Classifier150 (block 916). For the parameters sent to preliminary classifiers, theexample process 900 processes the parameter values by the algorithm typein each preliminary classifier (block 912). For example, parameters sentto a fuzzy logic preliminary classifier are processed via a fuzzy logicalgorithm to determine a characteristic from one or more combinedparameters about the media device. Upon processing parameter values, thepreliminary classifiers output parameter values (block 914) and send theoutputted parameter values to the Naïve Bayes Classifier 150 (block916).

The example process continues 900 when the Naïve Bayes Classifier 150loads the training data of conditional probabilities and/or weights fromthe training data memory (block 918). Next, the Naïve Bayes Classifier150 analyzes the parameter values and matches the correspondingconditional probabilities for each media device state to each parametervalue (block 920). Then, for each possible state of the media device,the conditional probabilities for each parameter are calculatedresulting in probabilities for each media device state (block 922). Thecalculation may include using equation (1) described in conjunction withFIG. 2 or a sum of logarithmic conditional probabilities of eachparameter value. Next, the Naïve Bayes Classifier 150 selects the statewith the highest probability and/or the greatest log odds (block 924).Then, the example process ends when the Naïve Bayes Module 128 outputsthe state of the media device (block 926).

FIG. 10 is a block diagram of an example computer system 1000 capable ofimplementing the apparatus and methods disclosed herein. The computer1000 can be, for example, a server, a personal computer, a personaldigital assistant (PDA), an internet appliance, a DVD player, a CDplayer, a digital video recorder, a personal video recorder, a set topbox, or any other type of computing device. Any or all of the exampleNaïve Bayes Module 128, the example Naïve Bayes Classifier 150, theexample input collector 202, and/or the example parameter generator 204may be implemented by the example computer 1000.

The system 1000 of the illustrated example includes a processor 1012such as a general purpose programmable processor. The processor 1012includes a local memory 1014, and executes coded instructions 1016present in the local memory 1014 and/or in another memory device. Thecoded instructions 1016 may include some or all of the instructionsimplementing the processes represented in FIGS. 7, 8, and 9. Theprocessor 1012 may be any type of processing unit, such as one or moremicroprocessors from the Intel® Centrino® family of microprocessors, theIntel® Pentium® family of microprocessors, the Intel® Itanium® family ofmicroprocessors, the Intel® Core® family of microprocessors, and/or theIntel® XScale® family of processors. Of course, other processors fromother families are also appropriate.

The processor 1012 is in communication with a main memory including avolatile memory 1018 and a non-volatile memory 1020 via a bus 1022. Thevolatile memory 1018 may be implemented by Static Random Access Memory(SRAM), Synchronous Dynamic Random Access Memory (SDRAM), Dynamic RandomAccess Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/orany other type of random access memory device. The non-volatile memory1020 may be implemented by flash memory and/or any other desired type ofmemory device. Access to the main memory 1018, 1020 is typicallycontrolled by a memory controller.

The computer 1000 also includes an interface circuit 1024. The interfacecircuit 1024 may be implemented by any type of interface standard, suchas an Ethernet interface, a universal serial bus (USB), and/or a thirdgeneration input/output (3GIO) interface.

One or more input devices 1026 are connected to the interface circuit1024. The input device(s) 1026 permit a user to enter data and commandsinto the processor 1012. The input device(s) can be implemented by, forexample, a keyboard, a mouse, a touchscreen, a track-pad, a trackball,an isopoint and/or a voice recognition system.

One or more output devices 1028 are also connected to the interfacecircuit 1024. The output devices 1028 can be implemented, for example,by display devices (e.g., a liquid crystal display, a cathode ray tubedisplay (CRT)), by a printer and/or by speakers. The interface circuit1024, thus, typically includes a graphics driver card.

The interface circuit 1024 also includes a communication device such asa modem or network interface card to facilitate exchange of data withexternal computers via a network (e.g., an Ethernet connection, adigital subscriber line (DSL), a telephone line, coaxial cable, acellular telephone system, etc.).

The computer 1000 also includes one or more mass storage devices 1030for storing software and data. Examples of such mass storage devices1030 include floppy disk drives, hard drive disks, compact disk drivesand digital versatile disk (DVD) drives. The mass storage devices 1030may implement any or all of the training data memory 208. Additionallyor alternatively, the volatile memory 1018 may implement any or all ofthe example training data memory 208.

At least some of the above described example methods and/or apparatusare implemented by one or more software and/or firmware programs runningon a computer processor. However, dedicated hardware implementationsincluding, but not limited to, application specific integrated circuits,programmable logic arrays and other hardware devices can likewise beconstructed to implement some or all of the example methods and/orapparatus described herein, either in whole or in part. Furthermore,alternative software implementations including, but not limited to,distributed processing or component/object distributed processing,parallel processing, or virtual machine processing can also beconstructed to implement the example methods and apparatus describedherein.

It should also be noted that the example software and/or firmwareimplementations described herein are stored on a tangible storagemedium, such as: a magnetic medium (e.g., a magnetic disk or tape); amagneto-optical or optical medium such as an optical disk; or a solidstate medium such as a memory card or other package that houses one ormore read-only (non-volatile) memories, random access memories, or otherre-writable (volatile) memories. A digital file attached to e-mail orother information archive or set of archives is considered adistribution medium equivalent to a tangible storage medium.Accordingly, the example software and/or firmware described herein canbe stored on a tangible storage medium or distribution medium such asthose described above or successor storage media.

To the extent the above specification describes example components andfunctions with reference to particular standards and protocols, it isunderstood that the scope of this patent is not limited to suchstandards and protocols. For instance, each of the standards forinternet and other packet switched network transmission (e.g.,Transmission Control Protocol (TCP)/Internet Protocol (IP), UserDatagram Protocol (UDP)/IP, HyperText Markup Language (HTML), HyperTextTransfer Protocol (HTTP)) represent examples of the current state of theart. Such standards are periodically superseded by faster or moreefficient equivalents having the same general functionality.Accordingly, replacement standards and protocols having the samefunctions are equivalents which are contemplated by this patent and areintended to be included within the scope of the accompanying claims.

Additionally, although this patent discloses example systems includingsoftware or firmware executed on hardware, it should be noted that suchsystems are merely illustrative and should not be considered aslimiting. For example, it is contemplated that any or all of thesehardware and software components could be embodied exclusively inhardware, exclusively in software, exclusively in firmware or in somecombination of hardware, firmware and/or software. Accordingly, whilethe above specification described example systems, methods and articlesof manufacture, the examples are not the only way to implement suchsystems, methods and articles of manufacture. Therefore, althoughcertain example methods, apparatus and articles of manufacture have beendescribed herein, the scope of coverage of this patent is not limitedthereto. On the contrary, this patent covers all methods, apparatus andarticles of manufacture fairly falling within the scope of the appendedclaims either literally or under the doctrine of equivalents.

What is claimed is:
 1. A method to determine a state of a media device, the method comprising: processing a first output signal from a first sensor measuring a first property of the media device to generate a first parameter value; processing a second output signal from a second sensor measuring a second property of the media device to generate a second parameter value; generating a first state probability by combining a first conditional probability that the first parameter value corresponds to the media device being in a first state and a second conditional probability that the second parameter value corresponds to the media device being in the first state; generating a second state probability by combining a third conditional probability that the first parameter value corresponds to the media device being in a second state and a fourth conditional probability that the second parameter value corresponds to the media device being in the second state; and determining a detected state of the media device by selecting a greater of the first state probability or the second state probability.
 2. A method as defined in claim 1, wherein the second sensor and the first sensor are the same sensor.
 3. A method as defined in claim 1, wherein the second parameter and the first parameter are the same parameter.
 4. A method as defined in claim 1, further comprising: determining the first conditional probability associated with the first parameter value and the second probability associated with the second parameter value by placing a training media device into the first state, recording a first set of first parameter values, recording a second set of second parameter values, calculating a first probability distribution of the first parameter values in the first set, and calculating a second probability distribution of the second parameter values in the second set; and determining the third conditional probability associated with the first parameter value and the fourth probability associated with the second parameter value by placing the training media device into the second state, recording a third set of first parameter values, recording a fourth set of second parameter values, calculating a third probability distribution of the first parameter values in the third set, and calculating a fourth probability distribution of the second parameter values in the fourth set.
 5. A method as defined in claim 4, wherein the first conditional probability corresponds to a first range of the set of the first parameter values and the second conditional probability corresponds to a second range of the set of the second parameter values.
 6. A method as defined in claim 1, further comprising transmitting the detected state of the media device to a meter by transmitting a confidence probability associated with the detected state of the media device.
 7. A method as defined in claim 6, wherein the confidence probability is the greater of the first state probability or the second state probability.
 8. A method as defined in claim 1, wherein processing the first output signal and processing the second output signal includes at least one of conditioning, normalizing, summing, averaging, filtering, normally distributing, parsing, amplifying, or converting.
 9. A method as defined in claim 1, further comprising: inputting at least one of the first parameter value or the second parameter value into a preliminary classifier that generates a preliminary output by processing the first parameter value or the second parameter value using a probabilistic algorithm; combining the first conditional probability associated with the first parameter value, the second conditional probability associated with the second parameter value, and the preliminary output for the first state of the media device prior to determining the first state probability for the first state of the media device; and combining the third conditional probability associated with the first parameter value, the fourth conditional probability associated with second parameter value, and the preliminary output for the second state of the media device prior to determining the second state probability for the second state of the media device.
 10. A method as defined in claim 9, wherein the probabilistic algorithm is at least one of a fuzzy logic algorithm, a decision tree algorithm, a neural network algorithm, an instance-based learning algorithm, or a rule induction algorithm.
 11. An apparatus to determine the state of a media device, the apparatus comprising: a parameter generator to process a first signal from a first sensor measuring a first property of the media device to generate a first parameter value and to process a second signal from a second sensor measuring a second property of the media device to generate a second parameter value; and a classifier to: generate a first state probability by combining a first conditional probability that the first parameter value corresponds to the media device being in a first state and a second conditional probability that the second parameter value corresponds to the media device being in the first state; generate a second state probability by combining a third conditional probability that the first parameter value corresponds to the media device being in a second state and a second conditional probability that the second parameter value corresponds to the media device being in the second state; and determine a detected state of the media device by selecting a greater of the first state probability or the second state probability.
 12. An apparatus defined in claim 11, further comprising: a training data memory to store the first conditional probability; and an input collector to receive the first signal from the first sensor and the second signal from the second sensor.
 13. An apparatus as defined in claim 11, wherein the classifier is trained by: determining the first conditional probability associated with the first parameter value and the second conditional probability associated with the second parameter value prior to receiving the first signal from the first sensor by placing a training media device into the first state, recording a first set of first parameter values, recording a second set of second parameter values, calculating a first probability distribution of the first parameter values in the first set, and calculating a second probability distribution of the second parameter values in the second set; and determining the third conditional probability associated with the first parameter value and the fourth conditional probability associated with the second parameter value prior to receiving the second signal from the second sensor by placing the training media device into the second state, recording a third set of first parameter values, recording a fourth set of second parameter values, calculating a third probability distribution of the first parameter values in the third set, and calculating a fourth probability distribution of the second parameter values in the fourth set.
 14. An apparatus as defined in claim 11, wherein the first sensor and the second sensor include at least one of an audio sensor, a visual sensor, a light sensor, a radio frequency sensor, an infrared sensor, a motion sensor, a vibration sensor, an angular rate sensor, an acceleration sensor, a pressure sensor, a voltage meter, a current meter, a power meter, a thermometer, a magnetic sensor, or a chemical sensor.
 15. An apparatus as defined in claim 11, wherein the second sensor and the first sensor are the same sensor.
 16. An apparatus as defined in claim 11, further comprising an input collector to receive a plurality of output signals from two or more sensors measuring a corresponding plurality of parameters of the media device.
 17. An apparatus as defined in claim 11, wherein the media device includes at least one of a television, a computer, a digital video recorder, a digital video disc player, a digital audio player, a cellular telephone, a laptop, a personal digital assistant, a portable video player, a gaming machine, a pocket personal computer, a satellite radio player, or an in-vehicle entertainment system.
 18. An apparatus as defined in claim 11, further comprising a preliminary classifier to receive at least one of the first parameter value or the second parameter value, generate a preliminary output by processing the first parameter value or the second parameter value using a probabilistic algorithm, and transmit the preliminary output to the classifier.
 19. An apparatus as defined in claim 18, wherein the probabilistic algorithm is at least one of a fuzzy logic algorithm, a decision tree algorithm, a neural network algorithm, an instance-based learning algorithm, or a rule induction algorithm.
 20. A tangible machine accessible storage device or disc comprising instructions that, when executed, cause a machine to at least: process a first output signal from a first sensor measuring a first property of the media device to generate a first parameter value; process a second output signal from a second sensor measuring a second property of the media device to generate a second parameter value; generate a first state probability by combining a first conditional probability that the first parameter value corresponds to the media device being in a first state and a second conditional probability that the second parameter value corresponds to the media device being in the first state; generate a second state probability by combining a third conditional probability that the first parameter value corresponds to the media device being in a second state and a fourth conditional probability that the second parameter value corresponds to the media device being in the second state; and determine a detected state of the media device by selecting a greater of the first state probability or the second state probability.
 21. A machine accessible storage device or disc as defined in claim 20, wherein the instructions, when executed, cause the machine to: determine the first conditional probability associated with the first parameter value and second conditional probability associated with the second parameter value by placing a training media device into the first state, recording a first set of first parameter values, recording a second set of second parameter values, calculating a first probability distribution of the first parameter values in the first set, and calculating a second probability distribution of the second parameter values in the second set; and determine the third conditional probabilities of the first parameter value and the second parameter value by placing the training media device into the second state, recording a third set of first parameter values, recording a fourth set of second parameter values, calculating a third probability distribution of the first parameter values in the third set, and calculating a fourth probability distribution of the second parameter values in the fourth set.
 22. A machine accessible storage device or disc as defined in claim 20, wherein the instructions, when executed, cause the machine to: process at least one of the first parameter value or the second parameter value into a preliminary output by processing the first parameter value or the second parameter value by a probabilistic algorithm; combine the first conditional probability associated with the first parameter value, the second conditional probability associated with the second parameter value, and the preliminary output for the first state of the media device prior to determining the first state probability for the first state of the media device; and combine the third conditional probability associated with the first parameter value, the fourth conditional probability associated with the second parameter value, and the preliminary output for the second state of the media device prior to determining the second state probability for the second state of the media device. 